The role of noise sensitivity in the noise–response relation: A comparison of three international airport studies
Bibliographic record
Abstract
In order to examine the role of noise sensitivity in response to environmental noise, this paper presents detailed comparisons of socio-acoustic studies conducted around international airports in Amsterdam, Sydney, and London. Earlier findings that noise sensitivity moderates the effect of noise on annoyance were examined to see if they could be replicated in each of the datasets, independent of the technique of measuring noise sensitivity. The relation between exposure to aircraft noise and noise annoyance was studied separately for groups of individuals with low, medium, and high noise sensitivity, with statistical adjustment for relevant confounders. Results support the previous findings that noise sensitivity is an independent predictor of annoyance and adds to the prediction of noise annoyance afforded by noise exposure level by up to 26% of explained variance. There is no evidence of a moderating effect, whereby the covariation between noise exposure level and annoyance is weak for people who score at the extreme high or low end of the sensitivity scale, and strong for people who score in the middle of the sensitivity scale. Generally, noise sensitivity appears to increase annoyance independently of the level of noise exposure after adjustment for relevant confounders. These findings were consistent across the three datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".